Exploratory Data Mining and Data Cleaning PDF Download

Author: Tamraparni Dasu
Publisher: John Wiley & Sons
ISBN: 0471458643
Category : Mathematics
Languages : en
Pages : 226

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Book Description
Written for practitioners of data mining, data cleaning and database management. Presents a technical treatment of data quality including process, metrics, tools and algorithms. Focuses on developing an evolving modeling strategy through an iterative data exploration loop and incorporation of domain knowledge. Addresses methods of detecting, quantifying and correcting data quality issues that can have a significant impact on findings and decisions, using commercially available tools as well as new algorithmic approaches. Uses case studies to illustrate applications in real life scenarios. Highlights new approaches and methodologies, such as the DataSphere space partitioning and summary based analysis techniques. Exploratory Data Mining and Data Cleaning will serve as an important reference for serious data analysts who need to analyze large amounts of unfamiliar data, managers of operations databases, and students in undergraduate or graduate level courses dealing with large scale data analys is and data mining.

Author: Glenn J. Myatt
Publisher: John Wiley & Sons
ISBN: 0470101016
Category : Mathematics
Languages : en
Pages : 294

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Book Description
A practical, step-by-step approach to making sense out of data Making Sense of Data educates readers on the steps and issues that need to be considered in order to successfully complete a data analysis or data mining project. The author provides clear explanations that guide the reader to make timely and accurate decisions from data in almost every field of study. A step-by-step approach aids professionals in carefully analyzing data and implementing results, leading to the development of smarter business decisions. With a comprehensive collection of methods from both data analysis and data mining disciplines, this book successfully describes the issues that need to be considered, the steps that need to be taken, and appropriately treats technical topics to accomplish effective decision making from data. Readers are given a solid foundation in the procedures associated with complex data analysis or data mining projects and are provided with concrete discussions of the most universal tasks and technical solutions related to the analysis of data, including: * Problem definitions * Data preparation * Data visualization * Data mining * Statistics * Grouping methods * Predictive modeling * Deployment issues and applications Throughout the book, the author examines why these multiple approaches are needed and how these methods will solve different problems. Processes, along with methods, are carefully and meticulously outlined for use in any data analysis or data mining project. From summarizing and interpreting data, to identifying non-trivial facts, patterns, and relationships in the data, to making predictions from the data, Making Sense of Data addresses the many issues that need to be considered as well as the steps that need to be taken to master data analysis and mining.

Author: Mark van der Loo
Publisher: John Wiley & Sons
ISBN: 1118897153
Category : Computers
Languages : en
Pages : 316

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Book Description
A comprehensive guide to automated statistical data cleaning The production of clean data is a complex and time-consuming process that requires both technical know-how and statistical expertise. Statistical Data Cleaning brings together a wide range of techniques for cleaning textual, numeric or categorical data. This book examines technical data cleaning methods relating to data representation and data structure. A prominent role is given to statistical data validation, data cleaning based on predefined restrictions, and data cleaning strategy. Key features: Focuses on the automation of data cleaning methods, including both theory and applications written in R. Enables the reader to design data cleaning processes for either one-off analytical purposes or for setting up production systems that clean data on a regular basis. Explores statistical techniques for solving issues such as incompleteness, contradictions and outliers, integration of data cleaning components and quality monitoring. Supported by an accompanying website featuring data and R code. This book enables data scientists and statistical analysts working with data to deepen their understanding of data cleaning as well as to upgrade their practical data cleaning skills. It can also be used as material for a course in data cleaning and analyses.

Author: Glenn J. Myatt
Publisher: John Wiley & Sons
ISBN: 1118422104
Category : Mathematics
Languages : en
Pages : 262

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Book Description
Praise for the First Edition “...a well-written book on data analysis and data mining that provides an excellent foundation...” —CHOICE “This is a must-read book for learning practical statistics and data analysis...” —Computing Reviews.com A proven go-to guide for data analysis, Making Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining, Second Edition focuses on basic data analysis approaches that are necessary to make timely and accurate decisions in a diverse range of projects. Based on the authors’ practical experience in implementing data analysis and data mining, the new edition provides clear explanations that guide readers from almost every field of study. In order to facilitate the needed steps when handling a data analysis or data mining project, a step-by-step approach aids professionals in carefully analyzing data and implementing results, leading to the development of smarter business decisions. The tools to summarize and interpret data in order to master data analysis are integrated throughout, and the Second Edition also features: Updated exercises for both manual and computer-aided implementation with accompanying worked examples New appendices with coverage on the freely available TraceisTM software, including tutorials using data from a variety of disciplines such as the social sciences, engineering, and finance New topical coverage on multiple linear regression and logistic regression to provide a range of widely used and transparent approaches Additional real-world examples of data preparation to establish a practical background for making decisions from data Making Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining, Second Edition is an excellent reference for researchers and professionals who need to achieve effective decision making from data. The Second Edition is also an ideal textbook for undergraduate and graduate-level courses in data analysis and data mining and is appropriate for cross-disciplinary courses found within computer science and engineering departments.

Author: Suresh Kumar Mukhiya
Publisher: Packt Publishing Ltd
ISBN: 178953562X
Category : Computers
Languages : en
Pages : 342

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Book Description
Discover techniques to summarize the characteristics of your data using PyPlot, NumPy, SciPy, and pandas Key FeaturesUnderstand the fundamental concepts of exploratory data analysis using PythonFind missing values in your data and identify the correlation between different variablesPractice graphical exploratory analysis techniques using Matplotlib and the Seaborn Python packageBook Description Exploratory Data Analysis (EDA) is an approach to data analysis that involves the application of diverse techniques to gain insights into a dataset. This book will help you gain practical knowledge of the main pillars of EDA - data cleaning, data preparation, data exploration, and data visualization. You’ll start by performing EDA using open source datasets and perform simple to advanced analyses to turn data into meaningful insights. You’ll then learn various descriptive statistical techniques to describe the basic characteristics of data and progress to performing EDA on time-series data. As you advance, you’ll learn how to implement EDA techniques for model development and evaluation and build predictive models to visualize results. Using Python for data analysis, you’ll work with real-world datasets, understand data, summarize its characteristics, and visualize it for business intelligence. By the end of this EDA book, you’ll have developed the skills required to carry out a preliminary investigation on any dataset, yield insights into data, present your results with visual aids, and build a model that correctly predicts future outcomes. What you will learnImport, clean, and explore data to perform preliminary analysis using powerful Python packagesIdentify and transform erroneous data using different data wrangling techniquesExplore the use of multiple regression to describe non-linear relationshipsDiscover hypothesis testing and explore techniques of time-series analysisUnderstand and interpret results obtained from graphical analysisBuild, train, and optimize predictive models to estimate resultsPerform complex EDA techniques on open source datasetsWho this book is for This EDA book is for anyone interested in data analysis, especially students, statisticians, data analysts, and data scientists. The practical concepts presented in this book can be applied in various disciplines to enhance decision-making processes with data analysis and synthesis. Fundamental knowledge of Python programming and statistical concepts is all you need to get started with this book.

Author: Hadley Wickham
Publisher: "O'Reilly Media, Inc."
ISBN: 1491910364
Category : Computers
Languages : en
Pages : 521

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Book Description
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

Author: Gábor Békés
Publisher: Cambridge University Press
ISBN: 1108483011
Category : Business & Economics
Languages : en
Pages : 741

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Book Description
A comprehensive textbook on data analysis for business, applied economics and public policy that uses case studies with real-world data.

Author: Dorian Pyle
Publisher: Morgan Kaufmann
ISBN: 9781558605299
Category : Computers
Languages : en
Pages : 566

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Book Description
This book focuses on the importance of clean, well-structured data as the first step to successful data mining. It shows how data should be prepared prior to mining in order to maximize mining performance.

Author: Haldorai, Anandakumar
Publisher: IGI Global
ISBN: 1522575235
Category : Computers
Languages : en
Pages : 347

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Book Description
Recently, there has been a rapid increase in interest regarding social network analysis in the data mining community. Cognitive radios are expected to play a major role in meeting this exploding traffic demand on social networks due to their ability to sense the environment, analyze outdoor parameters, and then make decisions for dynamic time, frequency, space, resource allocation, and management to improve the utilization of mining the social data. Cognitive Social Mining Applications in Data Analytics and Forensics is an essential reference source that reviews cognitive radio concepts and examines their applications to social mining using a machine learning approach so that an adaptive and intelligent mining is achieved. Featuring research on topics such as data mining, real-time ubiquitous social mining services, and cognitive computing, this book is ideally designed for social network analysts, researchers, academicians, and industry professionals.

Author: David Mertz
Publisher: Packt Publishing Ltd
ISBN: 1801074402
Category : Mathematics
Languages : en
Pages : 499

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Book Description
Think about your data intelligently and ask the right questions Key FeaturesMaster data cleaning techniques necessary to perform real-world data science and machine learning tasksSpot common problems with dirty data and develop flexible solutions from first principlesTest and refine your newly acquired skills through detailed exercises at the end of each chapterBook Description Data cleaning is the all-important first step to successful data science, data analysis, and machine learning. If you work with any kind of data, this book is your go-to resource, arming you with the insights and heuristics experienced data scientists had to learn the hard way. In a light-hearted and engaging exploration of different tools, techniques, and datasets real and fictitious, Python veteran David Mertz teaches you the ins and outs of data preparation and the essential questions you should be asking of every piece of data you work with. Using a mixture of Python, R, and common command-line tools, Cleaning Data for Effective Data Science follows the data cleaning pipeline from start to end, focusing on helping you understand the principles underlying each step of the process. You'll look at data ingestion of a vast range of tabular, hierarchical, and other data formats, impute missing values, detect unreliable data and statistical anomalies, and generate synthetic features. The long-form exercises at the end of each chapter let you get hands-on with the skills you've acquired along the way, also providing a valuable resource for academic courses. What you will learnIngest and work with common data formats like JSON, CSV, SQL and NoSQL databases, PDF, and binary serialized data structuresUnderstand how and why we use tools such as pandas, SciPy, scikit-learn, Tidyverse, and BashApply useful rules and heuristics for assessing data quality and detecting bias, like Benford’s law and the 68-95-99.7 ruleIdentify and handle unreliable data and outliers, examining z-score and other statistical propertiesImpute sensible values into missing data and use sampling to fix imbalancesUse dimensionality reduction, quantization, one-hot encoding, and other feature engineering techniques to draw out patterns in your dataWork carefully with time series data, performing de-trending and interpolationWho this book is for This book is designed to benefit software developers, data scientists, aspiring data scientists, teachers, and students who work with data. If you want to improve your rigor in data hygiene or are looking for a refresher, this book is for you. Basic familiarity with statistics, general concepts in machine learning, knowledge of a programming language (Python or R), and some exposure to data science are helpful.